Development of a simple HPLC–MS/MS method to simultaneously determine teriflunomide and its metabolite in human plasma and urine: Application to clinical pharmacokinetic study of teriflunomide sodium and leflunomide
Bibliographic record
Abstract
Abstract A simple high‐performance liquid chromatography coupled with tandem mass spectrometry method was developed and fully validated to simultaneously determine teriflunomide (TER) and its metabolite 4‐trifluoro‐methylaniline oxanilic acid (4‐TMOA) in human plasma and urine. Merely 50 μL plasma and 20 μL urine were employed in sample preparation using protein precipitation and direct dilution method, respectively. An Agilent Zorbax eclipse plus C18 column was selected to achieve rapid separation for TER and 4‐TMOA within 3 min. Electrospray ionization under multiple reaction monitoring was used to monitor the ion transitions for TER (m/z 269.0 → 159.9), 4‐TMOA (m/z 231.9 → 160.0), internal standard teriflunomide‐d4 (m/z 273.0 → 164.0) and 2‐amino‐4‐trifluoromethyl benzoic acid (m/z 203.8 → 120.1), operating in the negative ion mode. This method proved to have better accuracy and precision over concentration range of 10–5000 ng/mL in plasma as well as 10–10,000 ng/mL in urine. After a full validation, this method was successfully applied in a pharmacokinetic study of teriflunomide sodium and leflunomide in Chinese healthy volunteers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".